Evidence map›Paper›PMID 36117930›Full record

ArticleDiabetology international2022

Predictive model and risk engine web application for surgical site infection risk in perioperative patients with type 2 diabetes.

Masaya Koshizaka, Ryoichi Ishibashi, Yukari Maeda, Takahiro Ishikawa, Yoshiro Maezawa, Minoru Takemoto, Koutaro Yokote

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 51% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Masaya KoshizakaDepartment of Medicine, Division of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan.ORCID 0000-0001-7374-9993
Ryoichi IshibashiDivision of Diabetes, Metabolism and Endocrinology, Department of Endocrinology, Hematology, and Gerontology, Chiba University Hospital, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670 Japan.
Yukari MaedaDepartment of Medicine, Division of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan.
Takahiro IshikawaDepartment of Medicine, Division of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan.
Yoshiro MaezawaDepartment of Medicine, Division of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan.
Minoru TakemotoDivision of Diabetes, Metabolism and Endocrinology, Department of Endocrinology, Hematology, and Gerontology, Chiba University Hospital, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670 Japan.
Koutaro YokoteDepartment of Medicine, Division of Diabetes, Metabolism, and Endocrinology, Chiba University Hospital, Chiba, Japan.
Chiba University Hospital · JPKisarazu Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: To identify predictive factors for surgical site infection (SSI) in patients with type 2 diabetes and develop a prediction tool. Materials and methods: We retrospectively analyzed the perioperative blood glucose management of 105 patients with type 2 diabetes treated from 2016 to 2018 at Chiba University Hospital. The primary outcome was SSI onset within 30 postoperative days; moreover, predictive factors were identified using univariate analysis. Principal component analysis and logistic regression analysis were performed to prepare SSI predictive model using the identified predictive factors. The area under the receiver operating characteristic curve (AUC) was evaluated. Based on the predictive model, we developed a risk engine for SSI prediction. Results: Compared with patients without SSI ( Conclusions: The predictive model developed in this study could screen high-risk patients. It may be useful to prevent SSI in such patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s13340-022-00587-w.

Indexed as

Predictive modelRisk engine web applicationSurgical site infectionType 2 diabetes

Identifiers

PMID36117930
PMCPMC9477990
OpenAlexW4280574306

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.